Robust statistical methods are used to analyze data containing outliers to obtain accurate results. Robust statistics assume that your underlying distribution is normal. Econometric model is one of the models that uses robust statistical methods and in this project the researcher focused on robust standard errors where HA and HAC are determined. Various methods were applied on testing and dealing with heteroscedasticity and autocorrelation using the package sandwich, stats and lmtest in R. The results yielded showed that there was no element of heteroscedasticity and autocorrelation. The interpretation of the results was that the data was normally distributed. There were however, limitations in this project one of them being time limit preventing further research. The conclusion was that the robust statistical methods are significant in the field of research and in the world of mathematics generally as they help to obtain accurate and highly reliable results. However, there is need for further research in the field of Robust statistical methods especially on how to deal with the HA and HAC as it was not addressed by this research since the data was normally distributed.